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Self similar 3D distributions of point-particles, with a given quasifractaldimension D, were generated on a Menger sponge model and then compared with\textit{2dfGRS} and \textit{Virgo project} data\footnote{http://www.mso.anu.edu.au/2dFGRS/,http://www.mpa-garching.mpg.de/Virgo/}. Using the principle of local knowledge,it is argued that in a finite volume of space only the two-point minusestimator is acceptable in the correlation analysis of self similar spatialdistributions. In this sense, we have simplified the Pietronero-Labinicorrelative analysis by defining a K-minus estimator, which when applied to2dfGRS data revealed the quasifractal dimension $D\approx 2$ as expected. Inour approach the K-minus estimator is used only locally. Dimensions between D =1 and D = 1.7, as suggested by the standard $\xi (r)$ analysis, were found tobe fallacy of the method. In order to visualize spatial quasifractal objects,we created a small software program called \textit{RoPo} (''Rotate Points'').This program illustrates and manifests local correlative analysis in which thevisual inspection emerged as a first step and a key part of the method.Finally, we discuss importance and perspective of the visual inspection onavailable real and simulated distributions. It is also argued that results ofcontemporary cosmological simulations do not faithfully represent real data, asthey show a formation of ever increasing collapsars. We consent that 2dfGRSdata are reminiscent of some kind of underlying turbulence like effects inaction.

K-minus Estimator Approach to Large Scale Structure